Core I will be responsible for analysis of the sphingolipids in RAW264.7, primary macrophages, and other biological materials that are selected by the LIPID MAPS Consortium for "lipidomic" analysis. In Specific Aim 1: Employ lipidomics to advance mechanistic understanding of lipid metabolism, Core I will continue to develop new MS/MS methods to profile the "sphingolipidome," including validation of internal standards as they become available and identification of new subspecies. As part of this aim, we will also improve existing methods by use of orthogonal liquid chromatography and other approaches to achieve higher throughput and enhanced detection of minor components in complex lipid mixtures. In Specific Aim 2: Employ lipidomics to investigate macrophages and tissues under pathological conditions as disease models, Core I will help identify differences in sphingolipid pathways between normal and diseased cells and tissues and their correlation with patterns of gene expression. These studies will be conducted both by the quantitative LC ESI-MS/MS methods developed in Aim 2 and by development of methods for broader sphingolipid fingerprinting using high resolution MS. In Specific Aim 3: Develop lipid networks and maps from lipidomics data analysis, Core I will assist in the development of new and more comprehensive bioinformatic networks and maps of biochemical pathways, building on the sphingolipid prototypes that were developed by this Core (www.sphingomap.org and Merrill et al., Trends Biochem. Sci. in press) during the first period of funding. These will allow more facile comparison of sphingolipid metabolites with gene expression data in a web-based format. To clarify how observed changes in the amounts of a given metabolite reflect differences in de novo biosynthesis, turnover, and recycling, Core I will develop methods to study sphingolipid dynamics using stable isotope precursors and will assist other Cores, including Core B/C (Bioinformatics), to develop models of metabolic flux. Together, these studies will provide a more user-friendly and comprehensive platform for investigators to obtain "sphingolipidomic" data as well as models and tools for investigators to validate, analyze, visualize, and interpret such data sets. Because abnormalities in lipid metabolism are major contributors to disease, knowledge gained by this approach will be directly applicable to biomedical research for improved public health.